Experimenting with information retrieval methods in the recovery of feature-code SPL traces

Vale, Tassio; Almeida, Eduardo Santana de

doi:10.1007/s10664-018-9652-3

Cited by 15 publications

(2 citation statements)

References 42 publications

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“…We try to reduce bias by choosing different application domains. Furthermore, each system has a considerable history of development and use in research (Gargantini et al 2016;Ha and Zhang 2019;Krüger et al 2018;Krüger et al 2019;Liebig et al 2010;Medeiros et al 2018;Vale and Almeida 2019). Moreover, we choose systems of different sizes, which we measured by counting the total number of lines of code of their last release (excluding blank lines and comments).…”

Section: Datasetmentioning

confidence: 99%

Evolving software system families in space and time with feature revisions

et al. 2022

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Software companies commonly develop and maintain variants of systems, with different feature combinations for different customers. Thus, they must cope with variability in space. Software companies further must cope with variability in time, when updating system variants by revising existing software features. Inevitably, variants evolve orthogonally along these two dimensions, resulting in challenges for software maintenance. Our work addresses this challenge with ECSEST (Extraction and Composition for Systems Evolving in Space and Time), an approach for locating feature revisions and composing variants with different feature revisions. We evaluated ECSEST using feature revisions and variants from six highly configurable open source systems. To assess the correctness of our approach, we compared the artifacts of input variants with the artifacts from the corresponding composed variants based on the implementation of the extracted features. The extracted traces allowed composing variants with 99-100% precision, as well as with 97-99% average recall. Regarding the composition of variants with new configurations, our approach can combine different feature revisions with 99% precision and recall on average. Additionally, our approach retrieves hints when composing new configurations, which are useful to find artifacts that may have to be added or removed for completing a product. The hints help to understand possible feature interactions or dependencies. The average time to locate feature revisions ranged from 25 to 250 seconds, whereas the average time for composing a variant was 18 seconds. Therefore, our experiments demonstrate that ECSEST is feasible and effective.

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Section: Datasetmentioning

confidence: 99%

Evolving software system families in space and time with feature revisions

et al. 2022

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“…For instance, Corley et al [2] explore the use of deep learning applied to feature location by the usage of document vectors. The authors in [37] propose a research method comprised of two experiments to evaluate five information retrieval methods targeting the extraction of feature-code trace links. Binkley et al [6] further illustrate the benefits of using the learning to rank technique in both feature location and traceability by applying learning to rank algorithms to improve several feature models for software maintenance.…”

Section: Related Workmentioning

confidence: 99%